{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "image_path = \"../output_videos/cropped_image.jpg\"\n",
    "image = cv2.imread(image_path)\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# take the top half of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_half_image=  image[0: int(image.shape[0]/2), :]\n",
    "plt.imshow(top_half_image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster the image into two clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abdullah/python_virtual_env/cv_env/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reshape the image into 2d array\n",
    "image_2d = top_half_image.reshape(-1, 3)\n",
    "\n",
    "# perform k-means clustering with 2 clusters\n",
    "kmeans = KMeans(n_clusters=2, random_state=0)\n",
    "kmeans.fit(image_2d)\n",
    "\n",
    "# get the cluster labels\n",
    "labels = kmeans.labels_\n",
    "\n",
    "# reshape the labels into the orginal image shape\n",
    "clustered_image = labels.reshape(top_half_image.shape[0], top_half_image.shape[1])\n",
    "\n",
    "# Display the clustered image\n",
    "plt.imshow(clustered_image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "corner_clusters = [clustered_image[0, 0], clustered_image[0, -1], clustered_image[-1, 0], clustered_image[-1, -1]]\n",
    "non_player_cluster = max(set(corner_clusters), key=corner_clusters.count)\n",
    "print(non_player_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "player_cluster = 1-non_player_cluster\n",
    "print(player_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([171.38378378, 235.65405405, 142.8472973 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_[player_cluster]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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